Signal-processing method and a receiver

ABSTRACT

The invention relates to a method for processing a signal, in which method a sample set is formed ( 300 ) and a set of absolute values is formed ( 302 ). The method comprises A) arranging ( 304 ) the elements of the absolute value set in the ascending order; B) setting ( 306 ) a threshold value; C) determining ( 308 ) the number of elements of a reference set to be formed; D) forming ( 309 ) a reference set; E) computing ( 310 ) a reference value by multiplying the mean or median of the reference set by the threshold value; F) comparing ( 312 ) the greatest element of the reference set with the reference value; G) increasing ( 316 ) the number of reference set elements; H) reiterating the preceding steps D to G until the predetermined ending condition is fulfilled; I) forming ( 318 ) an accepted absolute value set and a sample set by deleting the greatest element from the reference set.

FIELD

The invention relates to telecommunications systems and to methods andreceivers used in them to process a signal.

BACKGROUND

In telecommunications systems, the quality of the received signals isaffected by quick, impulsive interference due to various factors, suchas multipath propagation, fading of transmitted signals, shadowing,near-far problem and co-channel interference. The impulses disturb thesignal-processing methods used in telecommunications systems, due towhich estimating the power of a received signal, for instance, may beunsuccessful.

Power estimation is used in radio systems, for instance in estimatingthe signal-noise ratio and setting threshold values as well as inautomatic amplification control. However, the impulses disturb classicalpower estimation algorithms and can lead to erroneous results.Cancelling impulsive interference is often a prerequisite for thereceivers to be able to operate at a sufficient accuracy. For instancein FFT (Fast Fourier Transformation) band-stop filters used in receiversit is important to find the suitable threshold value level, because theperformance of the band-stop filter depends to a great extent on thecorrect threshold level setting. Further, when signal and noisesubspaces are separated, different noise-attenuation methods are used inwhich subspaces are separated on the basis of information-theoreticalcriteria, such as Akaike and MDL.

It has become more and more common to use so-called robust, i.e.control-weighted methods for cancelling impulsive interference in thetelecommunications systems. The robust methods are not sensitive to bigchanges in individual observation values, such as impulse-likeinterference in a received signal. The prior art robust methods utilizeso-called order statistics, the basic idea of which is to detect andcancel observed interference by properties associated with anobservation set arranged on the basis of variable values. One prior artrobust method is so-called median-type filtering. This kind of prior artmedian-type filtering, used for power estimation, is described ingreater detail for instance in the publication by C. Tepedelenlio{hacekover (g)}lu, N. Sidiropoulos, G. B. Giannakis, “Median Filtering ForPower Estimation In Mobile Communications Systems”, Third IEEE SignalProcessing Workshop on Signal Processing Advances in WirelessCommunications, Taoyuan, Taiwan, Mar. 20-23, 2001, pp 229-231.

A drawback of the prior art solutions is, however, that the ability toendure impulsive interference is not sufficiently good. Thus, they arenot well suited for separating signal and noise subspaces, for instance.In a median-type method according to the prior art, a drawback is thatinformation on the interfering signal is needed in advance to be able toidentify the correct interference impulses. Furthermore, it is difficultto implement a median-type method in practice.

BRIEF DESCRIPTION

It is an object of the invention to provide a method and a receiverimplementing the method in such a way that drawbacks associated with theprior art can be reduced. This is achieved with a method for processinga signal in a telecommunications system, in which method a sample set isformed from received signals and a set of absolute values is formed fromabsolute values of sample set elements. The method of the inventioncomprises A) arranging the elements of the absolute value set in theascending order; B) setting a threshold value; C) determining the numberof elements of a reference set to be formed; D) forming a reference setcomprising a predetermined number of elements of the absolute value setin the ascending order; E) computing a reference value by multiplyingthe mean or median of the reference set by the threshold value; F)comparing the greatest element of the reference set with the referencevalue; G) increasing the number of reference set elements for forming anew reference set when the greatest reference set element is smallerthan the reference value; H) reiterating the preceding steps D to Guntil a predetermined ending condition is fulfilled; and I) forming anaccepted absolute value set and a corresponding sample set by deletingthe greatest element from the reference set when the predeterminedending condition is fulfilled.

The invention also relates to a receiver comprising means for forming asample set from received signals and means for forming a set of absolutevalues from the absolute values of sample set elements. The receiveraccording to the invention comprises means A) for arranging the elementsof the absolute value set in the ascending order, B) for setting athreshold value; C) for determining the number of elements of areference set to be formed; D) for forming a reference set comprising apredetermined number of elements of the absolute value set in the orderof magnitude; E) for computing a reference value by multiplying the meanor median of the reference set by the threshold value; F) for comparingthe greatest element of the reference set with the reference value; G)for increasing the number of reference set elements for forming a newreference set when the greatest reference set element is smaller thanthe reference value; H) for reiterating the preceding steps D to G untila predetermined ending condition is fulfilled; I) for forming anaccepted absolute value set and a corresponding sample set by deletingthe greatest element from the reference set when the predeterminedending condition is fulfilled.

The preferred embodiments of the invention are disclosed in thedependent claims.

A plurality of advantages is achieved with the method and receiveraccording to the invention. A solution is provided that has calculatoryefficiency and is simple to implement. Advantages of the presentedsolution also include that the method operates at very high frequenciesof impulse interference and that the interference bandwidth can beincreased. The method also has good ability to endure impulsiveinterference: it functions even if up to 90% of the samples areinterfering impulses.

LIST OF DRAWINGS

In the following the invention will be described in greater detail inconnection with preferred embodiments, with reference to the attacheddrawings, in which

FIG. 1 shows an example of a telecommunications system according to theproposed solution;

FIG. 2 shows an example of a receiver according to the proposedsolution;

FIG. 3 shows a block diagram of a signal-processing method according tothe proposed solution;

FIG. 4 shows a block diagram of a signal-processing method according tothe proposed solution;

FIG. 5 shows a block diagram of a signal-processing method according tothe proposed solution.

DESCRIPTION OF EMBODIMENTS

Preferred embodiments of the invention can be applied totelecommunications systems that comprise one or more base stations and anumber of terminals communicating with one or more base stations. Onesuch a telecommunications system is the broadband WCDMA radio systememploying spread-spectrum data transmission. In the following,embodiments are described by using the radio system of FIG. 1 as theexample without being restricted to this, as will be obvious to a personskilled in the art.

The structure of a telecommunications system can be in essence as shownin FIG. 1. The telecommunications system comprises a base station 100and a plurality of generally mobile subscriber stations 102 to 106,which have bi-directional connections 108 to 112 to the base station100. The base station 100 switches the connections of the terminalequipment 102 to 106 to a base station controller 114, which forwardsthem to other parts of the system and to a fixed network. The basestation controller 114 controls the operation of one or more basestations. The base station controller 114 monitors the quality of theradio signal and the transmission power, and takes care of the handoverof the mobile station. In addition to the electronic components requiredfor transmitting and receiving radio communication, the base station 100also comprises signal processors, ASIC circuits and general-purposeprocessors, which take care of data transmission to the base stationcontroller 114 and control the operation of the base station 100. Thebase station 100 may comprise one or more transmitter/receiver units.The receiver of the proposed solution can be placed in connection witheither the base station 100 or the mobile subscriber stations 102 to106.

FIG. 2 shows an example of a receiver 200 according to the proposedsolution. The receiver comprises an A/D converter 202, asignal-processing block 204, an adapted filter 206 and a control unit208. The receiver may also comprise other means implementing receiveroperations, such as speech and channel encoders, modulators and RFparts. In addition, the device comprises an antenna 201, by means ofwhich signals are transmitted and received.

All operations of the receiver 200 are controlled by the control unit208, which is typically implemented by means of a microprocessor andsoftware or separate components. The A/D converter 202 converts thecontinuous signal received by the receiver 200 into a digital form. Theadapted filter 206 is a specific filter adapted to let only the desiredsignal pass through with slight signal attenuation and to block allother waveforms (including noise). Prior to the adapted filter 206 thesignals are at chip level and after the adapted filter at symbol level.The signal-processing block 204 is implemented by means of ASIC circuitsor a microprocessor and software. In the proposed solution it ispossible to place also other components between the signal-processingblock 204 and the adapted filter 206.

In FIG. 2, sampling, controlled by the control unit 208, in the A/Dconverter 202 takes place by reading the value of a continuous signal atgiven intervals. This sampling interval is generally constant indigitizing each signal. After digitizing the signal, thesignal-processing block 204 performs operations controlled by thecontrol unit 208.

In an embodiment of the invention, a sample set is first formed in thesignal-processing block 204 from the signals received from the A/Dconverter 202, and an absolute value set is formed from the absolutevalues of the sample set elements. In the signal-processing block 204,the elements of the absolute value set are arranged in the ascendingorder, a threshold value is set, and subsequently, the number ofreference set elements to be formed is determined, the reference setcomprising a predetermined number of elements of the absolute value setin the order of magnitude. In addition, a reference value is determinedin the signal-processing block 204, the reference value being theproduct of the mean of the absolute value set and a predeterminedthreshold value. Alternatively, the reference value is the product ofthe median of the absolute value set and the predetermined thresholdvalue. Next, in the signal-processing block 204, the value of thegreatest element of the reference set and the reference value arecompared with each other, after which the signal-processing block 204increases the number of reference set elements for forming a newreference set when the greatest element of the reference set is smallerthan the reference value. The signal-processing block 204 reiteratesthese measures until a predetermined ending condition is fulfilled.Subsequently, an accepted absolute value set and a correspondingreference set are formed in the signal-processing block 204 by deletingthe greatest element from the remaining reference set.

Since the above-mentioned ending condition is fulfilled when thegreatest element of the reference set is greater than the referencevalue, the measures taken in the receiver 200 cause only the noiselesssamples to be accepted from a received signal containing impulsiveinterference. Thus, the idea is to add the next greatest element of theabsolute value set to the reference formed from the elements of theabsolute value set each time the greatest element of the reference setis greater than the reference value. As long as the greatest element ofthe reference set is greater than the reference value, it is certainthat this greatest element is noiseless. This is possible becauseinterference impulses have greater absolute values than other samples.Since impulsive interference does not affect the signal quality,accurate power estimation, for example, can be performed for thereceived signal in the signal-processing block 204 on the basis of theaccepted absolute value set and the reference set.

FIG. 3 shows a block diagram of a signal-processing method according toan embodiment of the invention. In step 300, a sample set is formed fromthe received signals. The received signal comprises thermal noise andinterference impulses. An object of the method is to cancel theimpulsive interference from the received signal prior to processing thereceived signal further. This is implemented by dividing the receivedchip-level samples into a desired set and an interference set, which isdone by means of a threshold value. The desired set is thus theremaining sample set, from which the interferences have been cancelled.The sample set formed in step 300 is assumed to be Gaussian with zeromean, whereby the amplitude of the sample sequence isRayleigh-distributed. This situation is achieved in multiple-userDS-CDMA systems, in which the power control works. If, on the otherhand, there are only a few users, or the power control does not work,the mean of the variables deviates from zero, whereby the amplitude isRice-distributed.

After forming the sample set in step 300, the process proceeds to step302, in which an absolute value set is formed from the absolute valuesof the sample set elements. As the desired set of chip-level samples isassumed to be Gaussian with zero mean, the absolute value set consistingof the absolute values of the desired set is Rayleigh-distributed. Inpractice, the desired set is not precisely Gaussian, but the method issimplified by this assumption. The aim of the so-called robust methodsis not to find a real model of an adjustable system per se, but toachieve sufficiently good functioning of the system as a whole. Forinstance, if the sample set comprises a DS signal and thermal noise, theabsolute values of these samples are Rice-distributed. The proposedmethod can still be used in that case, too.

In step 304, the elements of the absolute value set are arranged in theascending order. Next, in step 306, a threshold value is set that isneeded later for computing a reference value. The threshold value isobtained from Rayleigh distribution. Selection of a suitable thresholdvalue is affected by the method used for computing the reference valuein a later step 310. If, for instance, the mean of the reference setformed in step 309 is used for computing the reference value and if itis desired that 0.1% of the sample set elements be deleted, thethreshold value is advantageously 2.97. If, on the other hand, it isdesired that 1% of the sample set elements be deleted, the thresholdvalue is 2.42. The assumption that the desired set is Gaussian with zeromean is sufficient for determining the threshold value. The thresholdvalue 2.97 is an acceptable value in all desired cases independent ofGaussian set variance. In a case of non-interference this particularthreshold value 2.97 causes a situation in which only 0.1% of thesamples of the desired set is erroneously selected to be impulses. Onthe other hand, if the median of the reference set formed in step 309 isused, instead of the mean of the reference set, for computing thereference value, and if it is desired that 0.1% of the sample setelements be deleted, the threshold value is advantageously 3.16.Further, if the median of the reference set is used and if it is desiredthat 1% of the sample set elements be deleted, the threshold value is2.58. The threshold value is independent of the Gaussian distributionvariance.

After setting the threshold value in step 306, the process proceeds tostep 308, where the number of elements in the reference set to be formedis determined. In step 309, a reference set is formed which comprisesabsolute value set elements, the number of which was determined in step308, in the order of magnitude. The number of elements determined instep 308 is determined to be suitable for the situation. Usually thenumber is at least three. However, in a case where 90% of the sample setelements are impulses, it is preferable to select at least 10% of theelements of the absolute value set as the number of reference setelements, whereby the reference set comprises 10% of all absolute valueelements in the order of magnitude.

In step 310, a reference value is computed by multiplying the mean ormedian of the reference set by a predetermined threshold value. Thus,the mean or median of the reference set is selected for computing thereference value. However, selection of the median results in an easiermethod, because it is simpler to compute than the mean value by takingonly the middlemost value of the reference set.

Next, in step 312, the greatest element of the reference set is comparedwith the reference value. In step 314, it is studied whether a givenending condition is fulfilled. This takes place for instance by studyingwhether the greatest element of the reference set is greater than thereference value. If it is observed in step 314 that the greatest valueof the reference set is smaller than the reference value, the endingcondition is not fulfilled, and one moves on to step 316, where thenumber of reference set elements is increased for forming a newreference set. The number to be increased is for example one, wherebythe new reference set formed in step 309 is supplemented with thatelement of the absolute value set which is next greatest compared withthe greatest element of the reference set. From step 314, the processthus moves on to step 309, where a new reference set is formed on thebasis of the number of elements selected to the new reference set,determined in step 316. After this, one moves on stepwise until in step314 the greatest element of the reference set is greater than thereference value or until steps 309, 310, 312, 314 and 316 have beenreiterated successively a predetermined number of times successively.The ending condition can thus be fulfilled for instance when thegreatest element of the reference set is greater than the referencevalue or when steps 309, 310, 312, 314 and 316 have been reiteratedsuccessively a predetermined number of times. If the ending condition isfulfilled in step 314, in other words if, for instance, the greatestelement of the reference set is greater than the reference value, onemoves on to step 318. In step 318, an accepted sample set is formed bydeleting the greatest element from the remaining reference set.

Thus, after setting the reference set, more elements of the absolutevalue set are taken to the reference set until a predetermined referencevalue is not exceeded. The method functions well, because theinterference impulses have higher absolute values than other sample setelements. Since the sample set variance does not have to be known whenthe threshold value is determined, the sample set can also be a directsequence signal, for example, which is below Gaussian noise with zeromean (thermal noise). Such is the case for instance in spread-spectrumsystems, where signal-noise ratio at chip level is below zero decibel.The method does not need any advance information on the interference.

When an accepted sample set has been formed in step 318, one can move onto process the obtained noiseless sample set further in a desiredmanner. Accurate power estimation, for example, is now possible. Powerestimation can be performed, for instance, by using a so-calledclassical power estimator, whereby the power estimation is performed onthe basis of the mean of the squares of the absolute values of theelements in the accepted sample set by using formula (1), or on thebasis of the mean of the squares of the elements in the acceptedabsolute value set by using formula (2): $\begin{matrix}{P_{clas} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{y_{i}}^{2}}}} & (1) \\{P_{clas} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}x_{i}^{2}}}} & (2)\end{matrix}$

-   -   wherein:    -   P_(clas) is power,    -   N is the number of elements,    -   |y_(i)| is the absolute value of the element of the accepted        sample set,    -   x_(i) is an element of the accepted absolute value set.

Power estimation can also be carried out by a method that is based onRayleigh distribution, whereby the power estimation is performed bymeans of formula (3): $\begin{matrix}{P_{ray} = {\frac{4}{\pi}\left( {\frac{1}{N}{\sum\limits_{i = 1}^{N}x_{i}}} \right)^{2}}} & (3)\end{matrix}$

-   -   wherein:    -   P_(ray) is power.

The above method is also suitable for cancelling interference andseparating the signal and noise subspaces, since the ability of themethod to endure impulsive interference is good. The proposed method canalso be used to sort the received signal to acceptable samples andsamples to be rejected.

Let us next study a method for processing a signal according to anembodiment of the invention, shown in FIG. 4. At first, the methodproceeds as the one shown in FIG. 3. In step 400, a sample set is formedfrom the received signals. Next, in step 402, an absolute value set isformed from the absolute values of the sample set elements. Further, instep 404 the elements of the absolute value set are arranged in theascending order; in step 406 a threshold value is set; and in step 408the number of reference set elements is determined. In step 409, areference set is formed which comprises elements of the absolute valueset, the number of which was determined in step 408, in the ascendingorder. The reference set formed in the example of step 409 contains anodd number of elements.

Next, the process proceeds to step 410, in which a reference value iscomputed by multiplying the predetermined threshold value by themiddlemost element of the reference set, i.e. the reference set median.Further, in step 412 the greatest element of the reference set iscompared with the reference value computed in step 410; and in step 414it is studied whether the ending condition is fulfilled. In step 414,the ending condition is fulfilled if the greatest element of thereference set is greater than the reference value. If the endingcondition is not fulfilled, in other words if the greatest element ofthe reference set is not greater than the reference value, one moves onto step 416, in which the number of elements of the reference set isincreased for forming a new reference set. After that, one returns fromstep 416 to step 409, in which a new reference set is formed on thebasis of the number of elements selected to the new reference set, whichnumber was determined in step 416. In step 416, the number of referenceset elements to be increased is at least two, or an even number, so thatthe number of elements remains odd. After step 409, one proceedsstepwise until the ending condition is fulfilled in step 414.

When the greatest element of the reference set is, in step 414, greaterthan the reference value, one moves on to step 417, where an acceptedabsolute value set is formed by deleting the greatest element from theremaining reference set. Next, in step 418, a second reference value isformed by multiplying the threshold value with the mean of themiddlemost element and the element preceding it in the accepted absolutevalue set. In step 420, the greatest element of the accepted absolutevalue set is compared with the second reference value; in other words,it is studied whether the greatest value of the accepted absolute valueset is greater than the second reference value. If, in step 420, it isobserved that the greatest element of the accepted absolute value set isgreater than the second reference value, one moves on to step 422, inwhich the greatest element is deleted from the accepted absolute valueset. From step 422, one can move on to step 424, where the remainingabsolute value set is accepted. If, in step 420, it is observed that thegreatest element of the accepted absolute value set is not greater thanthe second reference value, one moves on to step 424, where the acceptedabsolute value set and the accepted sample set corresponding to it arepreserved.

FIG. 5 shows what is called an adaptive embodiment of the invention. Themethod of FIG. 5 comprises defining preset parameters, which areobtained by, for example, performing the method shown in FIG. 3 once.Thus, the steps of FIG. 5 can be performed after the steps shown in FIG.3, for example. In steps 500 and 502, the preset parameters S and P arecomputed. S computed in step 500 is obtained by computing the sum of theelements of the accepted absolute value set obtained in the method ofFIG. 3, for example. Alternatively, a given value can be defined for S.Step 502 comprises computing P, which is the number of elements of theaccepted absolute value set. Alternatively, an appropriate value can bedefined for P as well.

In step 504 of FIG. 5, the next sample element is received. In step 506,a third reference value is formed by multiplying the predeterminedthreshold value by the quotient of S and P. In step 508, a forgettingparameter is set. A forgetting parameter, i.e. an exponential weightingfactor, is used in recursive algorithms of the smallest square sum, inwhich error vectors of the smallest square sum are computed by utilizingall preceding samples. If a sample element obtained long ago is giventhe same weight as the sample vector just received, poor sample elementsfrom the past can affect the current solution. A forgetting parameterallows determination of how the algorithms handle old samples, in otherwords how the weights of old and new samples are weighted. The value ofthe forgetting parameter is between zero and one. If, for instance, oneis set as the forgetting parameter, it does not affect the sampleelement values. Usually the value of the forgetting parameter is setbetween 0.9 and 1.

In step 510, the absolute value of the sample element is compared with athird reference value. In step 512, it is studied whether the absolutevalue of the sample element is smaller than the third reference value.If the absolute value of the sample value is not smaller than the thirdreference value, one moves on to step 516, where the sample element istreated as a deviating sample, i.e. an interference impulse. In step516, the deviating sample is set to zero, for example. From step 516,one returns to step 504 to receive the following sample element, afterwhich one proceeds stepwise again. If, on the other hand, the absolutevalue of the sample element is, in step 512, smaller than the thirdreference value, the process proceeds to step 514, where the values of Sand P are updated. In step 514, the values of S and P are updated bycomputing new values for S and P by means of the forgetting parameter.The new value for S is computed by multiplying the forgetting parameterby S and adding the value of the sample element. The new value for P iscomputed by multiplying the forgetting parameter by P and by addingnumber one. The purpose of the forgetting parameter is to allow themethod to converge towards the optimal reference value when thestatistics of the received samples change. If the absolute value of thesample element is greater than the reference value, in other words ifthe sample value is an impulse, parameters S and P are not updated.After step 514, one returns to step 504 to receive the following sampleelements, after which steps 504 to 516 are reiterated a desired numberof times or until there are no more sample elements. The desired numberfor reiterating steps 504 to 516 is one or more.

The adaptive method presented above allows, for example, interferencecancellation to be started always when required, without knowinganything about the interference level of the received signal in advance.The adaptive method also works well even if the signal-noise ratiochanges in the middle of signal reception. Since in the adaptive methodonly one sample element is handled at a time, also the delay level ofthe method is low.

The above embodiments of the invention work well at a very highfrequency of impulse interference, for example at a frequency value 0.5.The lower the frequency of impulse interference is, the better thepresented methods work. The methods also work in interference-freecases. The lower the impulse-noise frequency is, the better the methodswork, irrespective of the power of the impulse noise. Thus, alsolow-frequency interference can be cancelled with the method. The methodsfunction even if up to 80 to 90% of the samples are interferenceimpulses.

Although the invention has been described above with reference to theexample of the attached drawings, it is obvious that the invention isnot restricted to it but can be modified in a variety of ways within theinventive idea of the attached claims.

1-24. (canceled)
 25. A method for processing a signal in atelecommunications system, the method comprising forming a sample setfrom received signals and forming a set of absolute values from absolutevalues of sample set elements, the method further comprising: A)arranging the elements of the absolute value set in the ascending order;B) setting a threshold value; C) determining the number of elements of areference set to be formed; D) forming a reference set comprising apredetermined number of elements of the absolute value set in theascending order; E) computing a reference value by multiplying the meanor median of the reference set by the threshold value; F) comparing thegreatest element of the reference set with the reference value; G)increasing the number of reference set elements for forming a newreference set when the greatest reference set element is smaller thanthe reference value; H) reiterating the preceding steps D to G until apredetermined ending condition is fulfilled; I) forming an acceptedabsolute value set and a corresponding sample set by deleting thegreatest element from the reference set when the predetermined endingcondition is fulfilled.
 26. A method according to claim 25, wherein thethreshold value is set according to Rayleigh distribution.
 27. A methodaccording to claim 25, wherein the ending condition is fulfilled whenthe greatest element of the reference set is greater than the referencevalue.
 28. A method according to claim 25, the method further comprisingestimating the power of the received signal on the basis of the mean ofthe squares of the absolute values of the elements in the acceptedsample set.
 29. A method according to claim 25, the method furthercomprising estimating the power of the received signal on the basis ofthe sample set accepted with the Rayleigh distribution method.
 30. Amethod according to claim 25, the method being used for cancelling theinterference of the received signal.
 31. A method according to claim 25,the method being used for separating signal-noise subspaces of thereceived signal.
 32. A method according to claim 25, the method beingused for dividing the received signal into acceptable samples andsamples to be rejected.
 33. A method according to claim 25, whereinthere is an odd number of reference set elements, and the method furthercomprises: forming a second reference value by multiplying the thresholdvalue by the mean of the middlemost element and the element preceding itin the absolute value set; comparing the second greatest element of theabsolute value set with the second reference value; deleting thegreatest element from the accepted absolute value set when the greatestelement of the accepted absolute value set is greater than the secondreference value; preserving the accepted absolute value set and theaccepted sample set corresponding to it when the greatest element of theaccepted absolute value set is smaller than the second reference value.34. A method according to claim 25, the method further comprising: J)computing sum S of the elements of the accepted absolute value set; K)computing number P of elements of the accepted absolute value set; L)receiving the following sample element; M) forming a third referencevalue by multiplying the threshold value by the quotient of S and P; N)setting a forgetting parameter; O) comparing the absolute value of thesample element with a third reference value; P) computing a new value ofS by multiplying the forgetting parameter by S and by adding the sampleelement value when the absolute value of the sample element is smallerthan the third reference value; Q) computing a new value of P bymultiplying the forgetting parameter by P and by adding number one whenthe absolute value of the sample element is smaller than the thirdreference value; R) handling the sample value as a deviating sample whenthe absolute value of the sample element is greater than the thirdreference value; S) reiterating the preceding steps L to R a desirednumber of times or until there are no more sample elements.
 35. A methodaccording to claim 34, the method comprising setting the sample elementhandled as a deviating sample to be zero.
 36. A method according toclaim 34, wherein the forgetting parameter is a value between zero andone.
 37. A receiver comprising means for forming a sample set fromreceived signals and means for forming a set of absolute values from theabsolute values of sample set elements, wherein the receiver furthercomprises: A) arranging means for arranging the elements of the absolutevalue set in the ascending order; B) setting means for setting athreshold value; C) determining means for determining the number ofelements of a reference set to be formed; D) forming means for forming areference set comprising a predetermined number of elements of theabsolute value set in the order of magnitude; E) computing means forcomputing a reference value by multiplying the mean or median of thereference set by the threshold value; F) comparing means for comparingthe greatest element of the reference set with the reference value; G)increasing means for increasing the number of reference set elements forforming a new reference set when the greatest reference set element issmaller than the reference value; H) iterating means for reiterating thepreceding steps D to G until a predetermined ending condition isfulfilled; I) forming means for forming an accepted absolute value setand a corresponding sample set by deleting the greatest element from thereference set when the predetermined ending condition is fulfilled. 38.A receiver according to claim 37, wherein the setting means for settinga threshold value are configured to set the threshold value on the basisof Rayleigh distribution.
 39. A receiver according to claim 37, whereinthe receiver comprises means for observing that the greatest element isgreater than the reference set and means for indicating that the endingcondition has been fulfilled.
 40. A receiver according to claim 37,wherein the receiver comprises estimating means for estimating the powerof the received signal on the basis of the mean of the squares of theabsolute values in the accepted sample set.
 41. A receiver according toclaim 37, wherein the receiver comprises estimating means for estimatingthe power of the received signal on the basis of the sample set acceptedwith the Rayleigh distribution method.
 42. A receiver according to claim37, wherein the receiver is arranged to cancel interference of thereceived signal.
 43. A receiver according to claim 37, wherein thereceiver is arranged to separate signal-noise subspaces of the receivedsignal.
 44. A receiver according to claim 37, wherein the receiver isarranged to divide the received signal into acceptable samples andsamples to be rejected.
 45. A receiver according to claim 37, whereinthere is an odd number of elements in the reference set and the receiverfurther comprises: forming means for forming a second reference value bymultiplying the threshold value by the mean of the middlemost elementand the element preceding it in the accepted absolute value set;comparing means for comparing the greatest value of the acceptedabsolute value set with the second reference value; deleting means fordeleting the greatest element from the accepted absolute value set whenthe greatest element of the accepted absolute value set is greater thanthe second reference value; preserving means for preserving theremaining absolute value set and the accepted sample set correspondingto it when the greatest value of the accepted absolute value set issmaller than the second reference value.
 46. A receiver according toclaim 37, wherein the receiver further comprises: J) computing means forcomputing sum S of the elements of the accepted absolute value set; K)computing means for computing number P of elements of the acceptedabsolute value set; L) receiving means for receiving the followingsample element; M) forming means for forming a third reference value bymultiplying the threshold value by the quotient of S and P; N) settingmeans for setting a forgetting parameter; O) comparing means forcomparing the absolute value of the sample element with the thirdreference value; P) computing means for computing a new value of S bymultiplying the forgetting parameter by S and by adding the sample valuewhen the absolute value of the sample is smaller than the thirdreference value; Q) computing means for computing a new value of P bymultiplying the forgetting parameter by P and by adding number one whenthe absolute value of the sample element is smaller than the thirdreference value; R) handling means for handling the sample value as adeviating sample when the absolute value of the sample element isgreater than the third reference value; S) iterating means forreiterating the preceding steps L to R a desired number of times oruntil there are no more sample elements.
 47. A receiver according toclaim 46, wherein the receiver is arranged to set the sample elementhandled as a deviating sample to be zero.
 48. A receiver according toclaim 46, wherein the receiver is arranged to set the value of theforgetting parameter between zero and one.